Mostly, machine learning (ML) based automation systems are supervised systems, and primarily rely on labeled examples coded by analysts for learning specific tasks, such as text classification. The idea of using ML-based automation systems has led to significant contributions to domain adaptation and transfer learning (DA/TL) techniques. The DA/TL techniques leverage knowledge, such as labeled data, from one or multiple source domains to learn an accurate model for unlabeled data in a target domain.
Typically, systems that deploy DA/TL techniques for text classification work on the assumption that the source data is labeled and the target data is unlabeled. Such systems learn a common representation where distributions of the source and the target data look as similar as possible. In accordance with such common representation, a model (or a classifier) trained on the source data is expected to perform efficiently on the target data also. Learning such common representation is utilized to transfer knowledge from the source domain to the target domain, however, in certain scenarios, it may result in negative transfer of features as each domain comprises domain specific features which are highly discriminating only within a domain and thus, may negatively contribute to the learning in the target domain if transferred in a brute force manner.
Further, traditional methods of learning the common representation between the source domains to the target domain may exhibit limited performance characteristics. One challenge may be that such methods do not explicitly exclude source specific representations. Another challenge may be that such methods miss out the discriminative features in the target domain. Thus, an advanced technique to learn transferable representations may be desired that mitigates such negative transfer of features and aforesaid challenges.
Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through comparison of described systems with some aspects of the present disclosure, as set forth in the remainder of the present application and with reference to the drawings.